Method for computer-assisted determination of an optimum-fuel control of nozzles

ABSTRACT

Method for the computer-assisted determination of an optimum-fuel control of nozzles according to a control instruction b=Ax. A defined matrix transformation of starting constraints for the mass flow of the nozzles and of the minimization criterion thereby takes place in a computer-assisted manner, a data processing representation of a geometric description of the matrix-transformed starting constraints, a computer-assisted determination of limiting point sets of the geometric description of the starting constraints through a computer-assisted geometric search procedure in the vector space and the application of the matrix-transformed minimization criterion to the points of the limiting point sets.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims priority under 35 U.S.C. §119 of GermanPatent Application No. 103 11 779.2, filed on Mar. 18, 2003, thedisclosure of which is expressly incorporated by reference herein in itsentirety.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a method for the computer-assisteddetermination of an optimum-fuel control of nozzles. Such a method canbe used in particular for controlling the nozzles of a spacecraft, suchas, e.g., a satellite, a space probe, a space station or the like.

2. Discussion of Background Information

A method for the optimum-fuel control of the attitude and nutation of aspinning spacecraft is known from U.S. Pat. No. 6,347,262. An errorsignal is hereby determined via a determination of spinning rates andspinning angles and a torque applied to the spacecraft according to theresult for this error signal.

EP 0 977 687 B1 describes different possibilities for the optimum-fuelcomputer-assisted control of nozzles of a spacecraft. Mainly methods aredescribed thereby that contain a simplex algorithm, whereby on the otherhand it is already stated there that such a simplex algorithm isassociated with a high expenditure of computation time. The methoddescribed within the scope of that invention uses a method that dependson the simplex algorithm and starts from the formation of a simplextable. A dual simplex algorithm is ultimately used there to form anoptimum-fuel control vector. As an alternative possibility fordetermining an optimum-fuel control of nozzles, only a “table look-up”method is described in EP 0 977 687 B1, in which optimum-fuel nozzlearrangements are calculated and entered in a table, and a currentcontrol result is formed from these previously stored results for therespective current control case through the combination of storedresults. However, it is disadvantageous that in general an actualoptimum-fuel result is not found with this method.

SUMMARY OF THE INVENTION

It is therefore one aspect of the present invention to provide a methodfor the computer-assisted determination of an optimum-fuel control ofnozzles that manages with the lowest possible computation expenditureand nevertheless reliably leads to an optimum-fuel solution.

This aspect is attained through a method for the computer-assisteddetermination of an optimum-fuel control of nozzles according to acontrol instruction b=Ax, where b represents a desired m-dimensionalforces/torque vector, A represents an m×n-dimensional nozzle matrix, andx represents the sought n-dimensional nozzle control vector and thenozzle control vector should meet the minimization criterion

$J:=\left. {\sum\limits_{i = 1}^{i = n}x_{i}}\rightarrow\min \right.$The method further includes a defined matrix transformation of startingconstraints for the mass flow of the nozzles and of the minimizationcriterion that takes place in a computer-assisted manner, a dataprocessing representation of a geometric description of thematrix-transformed starting constraints takes place in acomputer-assisted manner, and through a computer-assisted geometricsearch procedure in the vector space, where a computer-assisteddetermination of limiting point sets of the geometric description of thestarting constraints takes place. Finally, the matrix-transformedminimization criterion is applied to the points of the limiting pointsets. The invention further includes a computer program for thecomputer-assisted determination of an optimum-fuel control of nozzlesaccording to a control instruction b=Ax, where b represents a desiredm-dimensional forces/torque vector, A represents an m×n-dimensionalnozzle matrix, and x represents the sought n-dimensional nozzle controlvector and the nozzle control vector should satisfy the minimizationcriterion

$J:=\left. {\sum\limits_{i = 1}^{i = n}x_{i}}\rightarrow\min \right.$The computer program contains a first program routine for thecomputer-assisted execution of a defined matrix transformation ofstarting constraints for the mass flow of the nozzles and theminimization criterion, a second program routine for thecomputer-assisted execution of a data processing representation of ageometric description of the matrix-transformed starting constraints, athird program routine for the computer-assisted execution of a geometricsearch procedure in the vector space for the computer-assisteddetermination of limiting point sets of the geometric description of thestarting constraints, and a fourth program routine for thecomputer-assisted application of the matrix-transformed minimizationcriterion to the points of the limiting point sets. Finally, theinvention includes a computer program product that includes a computerprogram product containing a machine-readable program carrier on whichthe above-noted computer program can be stored in the form ofelectronically readable control signals.

The present invention comprises a method for the computer-assisteddetermination of an optimum-fuel control of nozzles according to acontrol instruction b=Ax, whereby

-   -   b represents a desired m-dimensional forces/torque vector,    -   A represents an m×n-dimensional nozzle matrix and    -   x represents the sought n-dimensional nozzle control vector with        the aid of which a corresponding thrust is effected through the        associated controlled nozzles. The nozzle control vector should        thereby meet the minimization criterion

$J:=\left. {\sum\limits_{i = 1}^{i = n}x_{i}}\rightarrow\min \right.$This minimization criterion is the term to indicate that the fuelconsumption should be minimal, which is achieved through a minimizationof the nozzle control and thus also a minimization of the thrustgenerated overall through the respective nozzles.

According to the invention, the following process steps are now providedin the scope of this method:

-   -   A defined matrix transformation of starting constraints for the        mass flow of the nozzles and of the minimization criterion takes        place in a computer-assisted manner, whereby the starting        constraints and the minimization criterion are respectively        subjected to an analogous matrix transformation,    -   A data processing representation of a geometric description of        the matrix-transformed starting constraints takes place in a        computer-assisted manner,    -   Through a computer-assisted geometric search procedure in the        vector space, a computer-assisted determination of limiting        point sets of the geometric description of the starting        constraints takes place, and    -   The matrix-transformed minimization criterion is applied to the        points of the limiting point sets.

A simplex method or a comparable iterative method can thus be avoidedwithin the scope of the invention. Instead of an iterative approach, ageometric approach realized in a computer-assisted manner is selected.Through the defined matrix transformation, a geometric description ofthe problem is possible that permits a geometric location of anoptimum-fuel solution. Such a method can be much quicker than a solutionof the problem via customary simplex methods.

In particular it can be provided in the scope of the present inventionthat:

-   -   For the matrix transformation of the starting constraints for        the mass flow of the nozzles, a homogenous solution of the        control instruction according to x_(ho=)A_(o)r is defined,        whereby        -   A_(o): represents the n×(n−m) dimensional zero space matrix            of A and        -   r: represents an (n−m) dimensional vector of any real            numbers    -   Within the scope of the use of the matrix transformation of the        minimization criterion a computer-assisted calculation is made        of scalar products of a vector representation of points of the        limiting point set and the vector

${v_{d}^{T}:=\left\lbrack {\sum\limits_{j = 1}^{n}{A_{0{j1}}{\sum\limits_{j = 1}^{n}{A_{0{j2}}\mspace{14mu}\ldots\mspace{14mu}{\sum\limits_{j = 1}^{n}A_{0{jp}}}}}}} \right\rbrack},{p:={n - m}}$and

-   -   An optimum-fuel solution is calculated with the aid of the        vector r whose scalar product is minimal with the vector v_(d).

This is an example of how through the use of a matrix transformationwith the aid of the zero space matrix A₀, the minimization criterion aswell as the starting constraints can be suitably transformed. Theminimization criterion can thereby be essentially reduced to the simpleformation of scalar products of vectors, thus to a simple geometriccalculation specification.

A preferred further development of the present invention provides that

-   -   The matrix-transformed starting constraints for the mass flow of        the nozzles is converted in a computer-assisted manner into        allowable multi-dimensional value regions,    -   To determine the limiting point sets, a formation of at least        one multi-dimensional cut set of the individual allowable        multi-dimensional value region takes place, and    -   The limiting point sets are determined as those point sets that        limit the at least one cut set.

In this manner the number of the points to be considered in the scope ofthe method according to the invention can be clearly reduced, since onlythose points are considered that limit the at least one cut set. Thismeans a further reduction of the computation time required for themethod and thus a further advantage over the previously known methods.

Advantageously the above-mentioned method can be still furthersimplified and thus the necessary computation time can be furtherreduced in that

-   -   A repeated projection of the allowable multi-dimensional value        regions of the dimension p is made on a dimension p−1, until a        projection of the allowable value regions on limiting intervals        of a dimension p=1 has been achieved and    -   Subsequently a computer-assisted search procedure carries out a        computer-assisted determination of limiting point sets as cut        set of limiting intervals.

Through the repeated projection provided here of a dimension p on adimension p−1, lower and lower dimensions (p−1, p−2, p−3, etc.) are thusgradually reached in which the value regions in question arerepresented, and thus problems that can be handled more easily incomputational terms than they would be in the starting dimension p. Inprinciple when a suitable adequately reduced dimension p>p₁>1 isreached, the determination of the limiting point sets could already takeplace. However, the repeated projection is preferably carried out untila description of the dimension p=1 has been reached. Here the problemcan be solved in the easiest manner and with the lowest expenditure ofcomputation time.

Another subject of the current invention is a computer program for thecomputer-assisted determination of an optimum-fuel control of nozzlesaccording to a control instruction b=A_(x), whereby:

-   -   b represents a desired m-dimensional forces/torque vector,    -   A represents an m×n-dimensional nozzle matrix and    -   x represents the sought n-dimensional nozzle control vector and        the nozzle control vector should satisfy the minimization        criterion

$J:=\left. {\sum\limits_{i = 1}^{i = n}x_{i}}\rightarrow\min \right.$

According to the invention it is provided that the computer programcontains the following:

-   -   A first program routine for the computer-assisted execution of a        defined matrix transformation of starting constraints for the        mass flow of the nozzles and the minimization criterion,    -   A second program routine for the computer-assisted execution of        a data processing representation of a geometric description of        the matrix-transformed starting constraints,    -   A third program routine for the computer-assisted execution of a        geometric search procedure in the vector space for the        computer-assisted determination of limiting point sets of the        geometric description of the starting constraints,    -   A fourth program routine for the computer-assisted application        of the matrix-transformed minimization criterion to the points        of the limiting point sets.

Such a computer program is suitable for executing the above-mentionedmethod according to the invention. Other program routines can also beprovided within the scope of this computer program which are suitablefor executing one or more of the above-mentioned further developments ofthe method according to the invention.

A further subject of the present invention is a computer program productcontaining a machine-readable program carrier on which anabove-described computer program is stored in the form of electronicallyreadable control signals. The control signals can be stored in anysuitable form, the electronic readout can then take place accordinglythrough electrical, magnetic, electromagnetic, electro-optic, or otherelectronic methods. Examples of such program carriers are magnetictapes, diskettes, hard disks, CD-ROM or semiconductor components.

One aspect of the present invention includes a method forcomputer-assisted determination of an optimum-fuel control of nozzlesaccording to a control instruction b=Ax, where: b represents a desiredm-dimensional forces/torque vector. A represents an m×n-dimensionalnozzle matrix, and x represents a sought n-dimensional nozzle controlvector and the nozzle control vector should meet a minimizationcriterion of

$J:=\left. {\sum\limits_{i = 1}^{i = n}x_{i}}\rightarrow{\min.} \right.$The method includes computer generating a defined matrix transformationof starting constraints for a mass flow of the nozzles and of theminimization criterion, data processing a representation of a geometricdescription of the matrix transformation of starting constraints,searching, with a computer-assisted geometric search procedure in vectorspace, limiting point sets of the geometric description of the startingconstraints, and applying the matrix transformation of minimizationcriterion to the points of the limiting point sets. Moreover, the matrixtransformation of the starting constraints for the mass flow of thenozzles can include a homogenous solution of the control instructionaccording to x_(ho)=A_(o)r where A_(o): represents the n×(n−m)dimensional zero space matrix of A, and r: represents an (n−m)dimensional vector of real numbers. Additionally, the method can includecalculating, within a scope of a use of the matrix transformation of theminimization criterion, scalar products of a vector representation ofpoints of the limiting point set and the vector and

${v_{d}^{T}:=\left\lbrack {\sum\limits_{j = 1}^{n}{A_{0{j1}}{\sum\limits_{j = 1}^{n}{A_{0{j2}}\mspace{14mu}\ldots\mspace{14mu}{\sum\limits_{j = 1}^{n}A_{0{jp}}}}}}} \right\rbrack},{p:={n - m}}$and calculating an optimum-fuel solution with the aid of the vector rwhose scalar product is minimal with the vector v_(d). Furthermore, themethod can include converting the matrix transformation of the startingconstraints for the mass flow of the nozzles in a computer-assistedmanner into allowable multi-dimensional value regions, forming, todetermine the limiting point sets, at least one multi-dimensional cutset of individual allowable multi-dimensional value regions, anddetermining the limiting point sets as those point sets that limit theat least one cut set. The method can also include repeatedly projectingthe allowable multi-dimensional value regions of the dimension p on adimension p−1, until a projection of the allowable value regions onlimiting intervals of a dimension p=1 has been achieved, andsubsequently searching, with a computer-assisted search procedure, adetermination of limiting point sets as a cut set of limiting intervals.Yet another aspect of the invention includes a computer program for thecomputer-assisted determination of an optimum-fuel control of nozzlesaccording to a control instruction b=Ax, whereby b represents a desiredm-dimensional forces/torque vector, A represents an m×n-dimensionalnozzle matrix, and x represents a sought n-dimensional nozzle controlvector and the nozzle control vector should satisfy the minimizationcriterion of

$J:=\left. {\sum\limits_{i = 1}^{i = n}x_{i}}\rightarrow{\min.} \right.$The computer program further includes a first program routine fordefining a matrix transformation of starting constraints for a mass flowof the nozzles and the minimization criterion, a second program routinefor data processing a representation of a geometric description of thematrix transformation of the starting constraints, a third programroutine for the execution of a geometric search procedure in the vectorspace for the determination of limiting point sets of the geometricdescription of the starting constraints, and a fourth program routinefor the application of the matrix transformation minimization criterionto the points of the limiting point sets. Moreover, a computer programproduct containing a machine-readable program on which a computerprogram, as noted above, can be stored in the form of electronicallyreadable control signals. Another aspect of the invention includes acomputer control method to obtain optimum-fuel usage for nozzles basedon an m-dimensional forces/torque vector, m×n-dimensional nozzle matrix,and an n-dimensional nozzle control vector that meets a minimizationcriterion. The method includes generating a defined matrixtransformation of starting constraints for a mass flow of the nozzlesand of the minimization criterion, data processing a representation of ageometric description of the matrix transformation of startingconstraints, searching, with a geometric search procedure in vectorspace, limiting point sets of the geometric description of the startingconstraints, and applying the matrix transformation of minimizationcriterion to the points of the limiting point sets. Moreover, thecontrol instruction can be b=Ax, where b represents the desiredm-dimensional forces/torque vector, A represents the m×n-dimensionalnozzle matrix, and x represents the sought n-dimensional nozzle controlvector and the nozzle control vector should satisfy the minimizationcriterion of

$J:=\left. {\sum\limits_{i = 1}^{i = n}\; x_{i}}\rightarrow{\min.} \right.$Additionally, the matrix transformation of the starting constraints forthe mass flow of the nozzles can include a homogenous solution of thecontrol instruction according to x_(ho)=A_(o)r, where A_(o): representsthe n×(n−m) dimensional zero space matrix of A, and r: represents an(n−m) dimensional vector of real numbers. The method can further includecalculating, within a scope of a use of the matrix transformation of theminimization criterion, scalar products of a vector representation ofpoints of the limiting point set and the vector

${v_{d}^{T}:=\left\lbrack {\sum\limits_{j = 1}^{n}{A_{0{j1}}{\sum\limits_{j = 1}^{n}{A_{0{j2}}\mspace{14mu}\ldots\mspace{14mu}{\sum\limits_{j = 1}^{n}A_{0{jp}}}}}}} \right\rbrack},{p:={n - m}}$and calculating an optimum-fuel solution with the aid of the vector rwhose scalar product is minimal with the vector v_(d). Additionally, themethod can include converting the matrix transformation of the startingconstraints for the mass flow of the nozzles into allowablemulti-dimensional value regions, forming, to determine the limitingpoint sets, at least one multi-dimensional cut set of individualallowable multi-dimensional value regions, and determining the limitingpoint sets as those point sets that limit the at least one cut set.Furthermore, the method can include repeatedly projecting the allowablemulti-dimensional value regions of the dimension p on a dimension p−1,until a projection of the allowable value regions on limiting intervalsof a dimension p=1 has been achieved, and subsequently searching adetermination of limiting point sets as a cut set of limiting intervals.Another aspect of the invention includes a computer program fordetermining an optimum-fuel control of nozzles according to a controlinstruction based on a desired m-dimensional forces/torque vector, anm×n-dimensional nozzle matrix, and a sought n-dimensional nozzle controlvector where the nozzle control vector should satisfy a minimizationcriterion. The computer program includes a first program routine fordefining a matrix transformation of starting constraints for a mass flowof the nozzles and the minimization criterion, a second program routinefor data processing a representation of a geometric description of thematrix transformation of the starting constraints, a third programroutine for the execution of a geometric search procedure in the vectorspace for the determination of limiting point sets of the geometricdescription of the starting constraints, and a fourth program routinefor the application of the matrix transformation minimization criterionto the points of the limiting point sets. Moreover, the controlinstruction can be b=Ax, where b represents the desired m-dimensionalforces/torque vector, A represents the m×n-dimensional nozzle matrix,and x represents the sought n-dimensional nozzle control vector and thenozzle control vector should satisfy the minimization criterion of

$J:=\left. {\sum\limits_{i = 1}^{i = n}\; x_{i}}\rightarrow{\min.} \right.$Also, a computer program product containing a machine-readable programon which the computer program noted above can be stored in the form ofelectronically readable control signals.

Other exemplary embodiments and advantages of the present invention maybe ascertained by reviewing the present disclosure and the accompanyingdrawing.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is further described in the detailed descriptionwhich follows, in reference to the noted plurality of drawings by way ofnon-limiting examples of exemplary embodiments of the present invention,in which like reference numerals represent similar parts throughout theseveral views of the drawings, and wherein:

FIG. 1 shows an allowable region for the vectors r in space of thedimension p2, whereby an inequation of constraint n_(i)^(T)(r×λ_(i)n_(i))≧0 is met;

FIG. 2 shows an allowable region for r in two-dimensional space takinginto account the starting constraints for several nozzles;

FIG. 3 shows an allowable region for r in the one-dimensional spacetaking into account the starting constraints for several nozzles;

FIG. 4 shows a cut set of two (p−1)-dimensional planes;

FIG. 5 shows linear coordinates transformation of unit vectors; and

FIG. 6 shows a comparison between computation time of a conventionalmethod according to a simplex algorithm and the method according to theinvention.

DETAILED DESCRIPTION OF THE PRESENT INVENTION

The particulars shown herein are by way of example and for purposes ofillustrative discussion of the embodiments of the present invention onlyand are presented in the cause of providing what is believed to be themost useful and readily understood description of the principles andconceptual aspects of the present invention. In this regard, no attemptis made to show structural details of the present invention in moredetail than is necessary for the fundamental understanding of thepresent invention, the description taken with the drawings makingapparent to those skilled in the art how the several forms of thepresent invention may be embodied in practice.

The invention relates to the following problem: desired forces and/ortorques are to be applied to a spacecraft through nozzles of thespacecraft. How should the nozzles be controlled so that the desiredforces and/or torques can be achieved with a minimal amount of fuel forthe nozzles? In addition the thrust of the nozzles has to lie in anallowable value region, thus between a minimum possible and a maximumpossible value. Accordingly, the control values must therefore also liein an allowable value region.

This problem is a so-called linear optimization problem. Hithertosimplex algorithms were mostly used to solve such problems, as describedat the outset, but they also exhibit the disadvantages describedlikewise at the outset. The present invention represents an improvedpossibility for solving the linear optimization problem.

The linear optimization problem for an optimum-fuel nozzle control isbased on the equation:b=Ax  (2.1)whereby

-   -   b represents a desired m-dimensional forces/torque vector,    -   A represents an m×n-dimensional nozzle matrix and    -   x represents the sought n-dimensional nozzle control vector.

A minimization criterion is to be met for the sought unknown vector x,through which an optimum-fuel control is guaranteed, namely

$\begin{matrix}{J:=\left. {\sum\limits_{i = 1}^{i = n}x_{i}}\rightarrow\min \right.} & (2.2)\end{matrix}$

Furthermore, the values for the vector x must be in an allowable valueregion analogous to the allowable value region for the thrust of thenozzles:0≦x _(i)≦1 for i=1, . . . n.  (2.3)

Without restriction of the generality of the problem shown in (2.1), itcan be assumed that the number of the nozzles n is greater than thenumber m of the desired forces and/or torques.n>m  (2.4)and that the nozzle matrix A has full rank,rank(A)=m  (2.5)

An infringement of (2.5) would mean for example if all the nozzlespointed in exactly the same direction. However, this is always avoidedby a logical arrangement of the nozzles on the satellite.

Now the minimization criterion can be described:

The general solution of the equation (2.1) x_(g) can be written asfollows:x _(g) =x _(pa) +x _(ho)  (2.6)whereby

-   -   x_(pa) special solution of (2.1)    -   x_(ho) general solution of the homogenous equation of (2.1).

A special solution x_(pa) can be generated from (2.1) as follows:b=Ax=AA ^(T)(AA ^(T))⁻¹ Ax=AA ^(T)(AA ^(T))⁻¹ b=A(A ^(T)(AA ^(T))⁻¹b)=A(x _(pa))  (2.7)

From this the following special solution is obtained:x _(pa) =A ^(T)(AA ^(T))⁻¹ b  (2.8)

All the vectors x_(ho) form the general homogenous solution of theproblem, for which vectors the following applies:Ax_(ho)=0,  (2.9)

This homogenous solution can be written as follows:x_(ho)=A₀r  (2.10)whereby

-   -   A₀: n×(n−m) zero space matrix of A    -   r: (n−m) dimensional vector of any real numbers.

With the aid of the equations (2.6, 2.8, 2.10) the minimizationcriterion from (2.2) can now be written as

$\begin{matrix}{{J(r)} = {{\sum\limits_{j = 1}^{n}\left( x_{pa} \right)_{j}} + {v_{d}^{T}r}}} & (2.11) \\{with} & \; \\{v_{d}^{T}\left\lbrack {\sum\limits_{j = 1}^{n}{A_{0{j1}}{\sum\limits_{j = 1}^{n}{A_{0{j2}}\mspace{14mu}\ldots\mspace{14mu}{\sum\limits_{j = 1}^{n}A_{0{jp}}}}}}} \right\rbrack} & (2.12) \\{p:={n - {m.}}} & (2.13)\end{matrix}$

Equation (2.11) can now be interpreted as follows: for the permittedvalues of r the vector should be found whose scalar product becomesminimal with the vector v_(d). This results from the fact that thespecial solution as defined in equation (2.8) is no longer accessible toa further minimization. The present invention implements this search forthe optimal vector r in a computer-assisted manner with the aid of acorresponding program routine of a computer program.

Geometric description of the optimum-fuel solution:

The allowable values or the allowable region for r is obtained from thestarting constraints for the mass flow of the nozzles according to (2.3)through a matrix transformation. This is formally obtained by inserting(2.6, 2.8, 2.10) in 2.3)−x _(pa) ≦A ₀ r≦ 1 −x _(pa)  (2.14)whereby

-   -   1: n-dimensional vector in which all elements equal 1

Now a geometric description of the starting constraints thus transformedcan be made which in the scope of the present invention is realized interms of data processing. The equation (2.14) can thereby be writtenwith the following definitions in scalar form.

$\begin{matrix}\begin{matrix}{l_{i}:=x_{pai}} \\{u_{i} = {1 - x_{pai}}} \\{A_{0}\begin{bmatrix}a_{01}^{T} \\a_{02}^{T} \\\vdots \\a_{0n}^{T}\end{bmatrix}}\end{matrix} & \left( {2.15a\text{-}c} \right)\end{matrix}$

Thus the following is obtained:

$\begin{matrix}\begin{matrix}{{a_{01}^{T}\left( {r - {\frac{l_{i}}{a_{0i}^{T}a_{0i}}a_{0i}}} \right)} \geq 0} & \mspace{14mu} & {{{{for}\mspace{14mu} i} = 1},{\ldots\mspace{14mu} n}}\end{matrix} & (2.16) \\\begin{matrix}{{- {a_{0i}^{T}\left( {r - {\frac{u_{i}}{a_{0i}^{T}a_{0i}}a_{0i}}} \right)}} \geq 0} & \; & {{{{for}\mspace{14mu} i} = 1},{\ldots\mspace{14mu}{n.}}}\end{matrix} & (2.17)\end{matrix}$

If now (2.16, 2.17) is divided by |a_(0i)|—due to constraint (2.5) thisis always possible—and with the following definitions

$\begin{matrix}\begin{matrix}{n_{i}:=\frac{a_{0i}}{a_{0i}}} & \; & {{{{for}\mspace{14mu} i} = 1},{\ldots\mspace{14mu} n}}\end{matrix} & (2.18) \\\begin{matrix}{n_{i}:={- \frac{a_{0i}}{a_{0i}}}} & {\mspace{11mu}{{{{for}\mspace{14mu} i} = {n + 1}},{\ldots\mspace{14mu} 2n}}}\end{matrix} & (2.19) \\\begin{matrix}{\lambda_{i}:=\frac{l_{i}}{a_{0i}}} & \; & {{{{for}\mspace{14mu} i} = 1},{\ldots\mspace{14mu} n}}\end{matrix} & (2.20) \\\begin{matrix}{\lambda_{i}:=\frac{u_{i}}{a_{0i}}} & \; & {{{{for}\mspace{14mu} i} = {n + 1}},{\ldots\mspace{14mu} 2n}}\end{matrix} & (2.21)\end{matrix}$the equations (2.16, 2.17) can be written as follows:n _(i) ^(T)(r−λ _(i) n _(i))≧0 for i=1, . . . 2n  (2.22)

Thus for a certain value of i the equation (2.22) can now be interpretedas a one-dimensional or multi-dimensional plane and thus shownaccordingly in terms of data processing (see FIG. 1), whereby this planeis displaced by the vector λn_(i) with respect to the origin and isoriented perpendicular to the normal vector n_(i). In the case of FIG. 1this one-dimensional or multi-dimensional plane is represented as astraight line for reasons of simplicity, thus as a plane of thedimension 1. This one-dimensional plane limits the range of theallowable values or the allowable vectors for r. If the sign of λ_(i) isnegative, the zero vector is also contained in the allowable region forr; however if the sign of λ_(i) is positive, the zero vector is excludedfrom the allowable region. The normal vector n_(i) points in thedirection of the allowable region. The geometric description of theequation (2.22) is shown for p=2 in FIG. 1 for λ_(i)>0. It is noted thatfor each constraint i in (2.22) a counter-constraint i+n withn_(i+n)=−n_(i) exists (according to the starting constraints for theminimum and maximum mass flow of an individual nozzle), from which npairs of constraints result that define allowable “area strips,” asshown in FIG. 2.

The common cut set of all allowable regions determines that region inthe p-dimensional space in which r meets all 2n transformed startingconstraints or all constraints from (2.22). In FIG. 2 this is shown forn=3 nozzles, thus for 6 constraints. If the cut set is formed from allallowable regions, a convex range results as cut set, i.e. that forrespectively two points that lie within the cut set, all points also lieon a straight line between these two points within the cut set.

However, not only the region of the allowable values for r can bedescribed geometrically, but also the optimum value for r, r_(opt),which minimizes J in equation (2.11): as stated, J becomes minimal forthat vector r that features the smallest scalar product or the smallestprojection on the vector v_(d). In FIG. 2 this is given for the vector rthat belongs to the vertex, marked with a circle, that lies on thestraight line g₁ to which the normal vector n₁ belongs. The optimumpoint thus results as one of the vertices of the limiting lines of thecut set. The limiting lines thus form limiting point sets.

The geometric description of this two-dimensional example can bedirectly applied analogously to higher dimensions p.

If the problem is a three-dimensional problem, the n-transformedstarting constraints can be described as n area regions between n planepairs, whereby each plane pair comprises two planes parallel to oneanother and the area region lying between them represents the region forr that meets the relevant transformed starting constraints. The overallallowable region for r that meets all transformed starting constraintsresults as cut set of all n area regions and thus corresponds to athree-dimensional polygon. The optimum point also results here as one ofthe vertices of the limitation of the polygon which is now limited bylimiting surfaces as limiting point sets. The optimum point is in turnthat vertex whose vector r features the smallest scalar product with thevector v_(d).

If it is a one-dimensional problem, the regions that are limited by then transformed starting constraints can be described as one-dimensionalintervals. The n transformed starting constraints are then describedthrough limiting points. This is shown by way of example in FIG. 3. Thebrackets of the same size shown in FIG. 3 respectively form a pair oftransformed starting constraints. The lower limits of the startingconstraints are labeled with λ₁, . . . λ₃, the upper limits of thestarting constraints with λ₄, . . . λ₆ (equations 2.20, 2.21). In theexample of FIG. 3 a cut set interval exists as allowable region for rwhich at the same time meets all transformed starting constraints. Theoptimum value for r results in turn from a point from the limiting pointset, here as the right limiting point of the cut set interval. Thevector r belonging to this point in turn has the smallest scalar productwith the negative vector v_(d) also shown in FIG. 3.

The same interpretations and descriptions are applicable to dimensionsp>3.

Computer-assisted determination of an optimum-fuel solution:

A special example for determining an optimum-fuel solution will now beshown. A method is thereby executed as described in the scope of theinvention, i.e., in particular a computer-assisted determination takesplace of (p-1)-dimensional limiting point sets as geometric descriptionof the transformed starting constraints in the form of planes. Thisspecial example now describes a preferred further development of theinvention and contains in particular the following process steps:

-   -   1. Computer-assisted determination of a first of the determined        planes as limiting point set that probably contains the        optimum-fuel solution. For further detail for finding this plane        see below.    -   2. Computer-assisted application of a linear coordinate        transformation to all vectors r so that the normal vector of the        first plane is subsequently parallel to the p-th unit vector of        those unit vectors that span the vector space of the vectors r        and so that the normal vector points in the same direction as        this unit vector.    -   3. Computer-assisted calculation of cut sets in the form of        intersecting planes of the other determined planes with the        first plane. The p-th component of each intersecting plane        thereby has the same constant value as the p-th component of the        first plane after the linear coordinate transformation. The        clear geometric description of the intersecting planes can thus        be simplified to a dimension p_(new)=p−1. Thus only the first        p−1 components are still needed to describe a vector v_(d).    -   4. Computer-assisted test: is p_(new) different from 1? Then        steps 1-3 are carried out again.        -   Computer-assisted test: if p_(new)=1, then a            computer-assisted test is carried out on whether an interval            is present as cut set that represents an allowable region            for r. If no such interval is present, the process is ended,            a second plane is selected according to step 1 and the            process is carried out again. If such an interval is            present, a computer-assisted multiplication is carried out            of the limiting points of the interval with the vector v_(d)            now reduced to a vector of the dimension 1, thus to a            scalar. The smaller value of the results of the            multiplication is stored and, if the process has already            been conducted before for one or more other planes, compared            with previously stored values. If the value stored last is            smaller than a value stored previously, the process is            started again according to step 1 with a new plane and            correspondingly carried out. If the value stored last is not            smaller, then the value stored previously represents the            optimal solution.

The individual computer-assisted steps will now be considered in moredetail.

Determination of the first of the determined planes according to step 1:In principle the plane that contains the optimum-fuel solution cannot beclearly determined a priori. However, it can be clarified using theexample of the case of p=2 that certain fundamental statements arepossible on the sought (p−1)-dimensional plane or straight line (in thecase p=2 the sought plane thus has the dimension 1). For geometricreasons it is clear that the optimum-fuel solution always lies on one ofthe straight line sections that limit the cut set of all allowableregions, namely on those straight lines whose normal vector (whichpoints in the direction of the allowable region) has the greatest scalarproduct with the vector v_(d) and whose direction thus best correspondsto the direction of the vector v_(d). In FIG. 2 the normal vector n₁best corresponds to the vector v_(d), so that the vector v_(d) featuresthe greatest scalar product with this normal vector. This scalar productcan be calculated in a computer-assisted manner and the correspondingnormal vector and thus the relevant (p−1)-dimensional plane can bedetermined in a computer-assisted manner. In this method with respect toFIG. 2 it is therefore assumed that the optimum-fuel solution lies onthe straight line section that limits the cut set and is characterizedby the normal vector n₁. The approach is analogous in all the cases ofthis method.

Thus in particular the following process steps are carried out:

-   -   Determination of the normal vector whose scalar product is        maximum with the vector v_(d).        -   Determining whether the relevant straight line (or in            general: (p−1)-dimensional plane) represents a limiting            point set of the cut set (see below). If not, this straight            line is abandoned and the straight line is selected whose            normal vector features the second largest scalar product            with the vector v_(d). Then this straight line is tested            again to see whether it represents a limiting point set.

Coordinate transformation of the first plane:

In order to simplify the computer-assisted determination of theintersecting planes which results as cut set of the individual(p−1)-dimensional planes, a new coordinate system is introduced. This isshown in FIG. 4. The coordinate system is rotated through a linearcoordinate transformation such that a unit vector (here the unit vectorwith the highest index, i.e., index p is selected) coincides with thenormal vector of the plane that was selected as the first plane in thestep described above. Through this coordinate transformation anadvantage results that the p-th coordinate of all intersecting planesnow feature the same constant value. FIG. 4 shows two levels p₁ and p₂that in the case of FIG. 4 feature an intersecting plane g₁₂ of thedimension 1, thus a straight line as cut set. Let p₁ now be the firstplane selected according to the method described above. Then through acoordinate transformation the unit vector e₃ is rotated by 180° aroundthe vector e₃+n₁ in order to obtain a transformed unit vector e₃′ thatnow coincides with the vector n₁ (see FIG. 5). The third coordinate ofg₁₂ then becomes a constant. This coordinate transformation can beexpressed as:e′ ₃ =n _(n)=└2dd ^(T) −E┘e ₃  (2.23)with

$\begin{matrix}{d:=\frac{e_{3} + n_{1}}{{e_{3} + n_{1}}}} & (2.24)\end{matrix}$

The transformation equation (2.23) can be generalized from threedimensions to a p-dimensional problem withe′ _(p) =n _(i)=└2dd ^(T) −E┘e _(p)  (2.25)with

$\begin{matrix}{d:=\frac{e_{p} + n_{i}}{{e_{p} + n_{i}}}} & (2.26)\end{matrix}$whereby i is the index of the selected plane.

Determination of the intersecting planes/cut sets:

Equations for describing the cut sets can be determined in the form ofplane equations. The plane equation for each plane i in thep-dimensional space can be described as:

$\begin{matrix}{{{\left\lbrack {n_{i1}n_{i2}n_{i3}\mspace{14mu}\ldots\mspace{14mu} n_{{ip} - 1}n_{ip}} \right\rbrack\begin{bmatrix}r_{1} \\r_{2} \\r_{3} \\r_{p - 1} \\r_{p}\end{bmatrix}} - \lambda_{i}} = 0} & (2.27)\end{matrix}$

The cut set of this plane with a selected plane j is described by theequation:r_(p)=λ_(j)  (2.28)

Thus this is obtained as a description of the cut set in the form of anintersecting plane:

$\begin{matrix}{{{\left\lbrack {n_{i1}n_{i2}n_{i3}\mspace{14mu}\ldots\mspace{14mu} n_{{ip} - 1}} \right\rbrack\begin{bmatrix}r_{1} \\r_{2} \\r_{3} \\r_{p - 1}\end{bmatrix}} + {n_{ip}\lambda_{j}} - \lambda_{i}} = 0.} & (2.29)\end{matrix}$

Equation (2.29) can be transcribed in the form of the equation (2.27) togiven _(i) ^(T) r− λ _(i)=0.  (2.30)with

$\begin{matrix}{{\overset{\_}{n}}_{i}:={\begin{bmatrix}n_{i1} \\n_{i2} \\n_{i3} \\n_{{ip} - 1}\end{bmatrix}*\frac{1}{\sqrt{1 - n_{ip}^{2}}}}} & (2.31) \\{\overset{\_}{r}:=\begin{bmatrix}r_{1} \\r_{2} \\r_{3} \\r_{p - 1}\end{bmatrix}} & (2.32) \\{{\overset{\_}{\lambda}}_{i}:=\frac{\lambda_{i} - {n_{ip}\lambda_{j}}}{\sqrt{1 - n_{ip}^{2}}}} & (2.33)\end{matrix}$

Computer-assisted search procedure:

The computer-assisted search procedure can now in principle contain thefollowing steps:

-   -   1. Computer-assisted determination of the most promising first        plane as start plane which most likely contains the optimum-fuel        solution and storage of the vector that points from the point of        origin to this plane.    -   2. Computer-assisted execution of a coordinate transformation as        described above and computer-assisted determination of        intersecting planes as also described above.    -   3. With the determined intersecting planes the process steps        described above are then executed until the dimension is reduced        to p=1.    -   4. It is then determined whether an allowable interval is        present (see above). If this is given, according to the above        description a point is determined as description of an        optimum-fuel solution and through a corresponding        back-transformation of the coordinates the optimum-fuel solution        is calculated in a computer-assisted manner.    -   5. Repetition of steps 1 through 4 until no further improvement        is obtained. The solution then present represents the global        optimum-fuel solution.

FIG. 6 shows within the framework of a calculation example a comparisonbetween the computation time of a conventional method M1 according to asimplex algorithm and the method M2 according to the invention,respectively for arrangements with 4, 5, 6, 7 or 8 nozzles. As isalready clear from FIG. 6 a), in this example the computation time forthe method M2 according to the invention for the cases with 4, 5 and 6nozzles is clearly shorter than the computation time of the simplexmethod M1. FIG. 6 b) illustrates the computation time M1/computationtime M2 ratio. The computation time of M1 is slower than the methodaccording to the invention by a factor of 100 for 4 nozzles, by a factorof 16 for 5 nozzles and by a factor of 3 for 6 nozzles. Only for highernumbers of nozzles is the method M2 according to the invention slowerthan the simplex method in this example.

If the optimal solution in the one-dimensional space is to be determined(i.e., if the difference between the number of nozzles and the number ofthe constraints equals 1), the above description of the method can bebrought into a more compact form. Then equation (2.14) results as nequations for the upper limit and lower limit, thus as the allowableregion of the transformed starting constraints−x _(pa) ≦a ₀ r≦ 1 −x _(pa)  (2.23)with the n-dimensional zero space vector a₀ and the unknown scalar r.

If a component a_(0i) is equal to zero, 0<=x_(pai)<=1 must apply for thespecial solution, otherwise the above-mentioned constraint cannot bemet. However, this case is excluded here, since this case in practicewould mean a badly selected nozzle arrangement.

The equations (2.23) are divided by the components a_(0i) (taking intoaccount the sign of a_(0i)), and the result isr_(min)≦r≦r_(max)  (2.24)with

$\begin{matrix}{r_{\min\; i}:=\begin{Bmatrix}{{{- \frac{x_{pai}}{a_{0i}}}\mspace{14mu}{for}\mspace{14mu} a_{0i}} > 0} \\{{\frac{1 - x_{pai}}{a_{0i}}\mspace{14mu}{for}\mspace{14mu} a_{0i}} < 0}\end{Bmatrix}} & (2.25) \\{r_{\max\; i}:=\begin{Bmatrix}{{\frac{1 - x_{pai}}{a_{0i}}\mspace{14mu}{for}\mspace{11mu} a_{0i}} > 0} \\{{{- \frac{x_{pai}}{a_{0i}}}\mspace{14mu}{for}\mspace{14mu} a_{0i}} < 0}\end{Bmatrix}} & (2.26)\end{matrix}$

The constraints in (2.24) can only be met at the same time ifmax(r _(min))≦min(r _(max))  (2.27)

The optimum solution for r, r_(opt), depends on the sign of the scalarv_(d) in the minimization criterion (2.11): for v_(d) greater than zero,the minimization criterion is as small as possible if r is selected onthe left edge of the interval (2.27),r _(opt)=max(r _(min)) for v_(d)>0  (2.28)and on the other hand for v_(d) smaller than zero, the minimizationcriterion is as small as possible if r is selected on the right edge ofthe interval (2.27)r _(opt)=min(r _(max)) for v_(d)<0  (2.29)

For v_(d)=0 any value for r can be selected from the interval (2.27) inorder to meet the minimization criterion.

The values for a₀ and v_(d) depend only on the position and thrustdirection of the nozzles, but not on the current thrust demands. Thecase discrimination according to (2.25, 2.26, 2.28, 2.29) thereforeneeds to occur only once before the computer-assisted execution of themethod to determine the optimal solution.

It is noted that the foregoing examples have been provided merely forthe purpose of explanation and are in no way to be construed as limitingof the present invention. While the present invention has been describedwith reference to an exemplary embodiment, it is understood that thewords which have been used herein are words of description andillustration, rather than words of limitation. Changes may be made,within the purview of the appended claims, as presently stated and asamended, without departing from the scope and spirit of the presentinvention in its aspects. Although the present invention has beendescribed herein with reference to particular means, materials andembodiments, the present invention is not intended to be limited to theparticulars disclosed herein; rather, the present invention extends toall functionally equivalent structures, methods and uses, such as arewithin the scope of the appended claims.

1. A method for controlling nozzles based upon computer-assisteddetermination of an optimum-fuel control of the nozzles according to acontrol instruction b=Ax, whereby b represents a desired m-dimensionalforces/torque vector, A represents an m×n-dimensional nozzle matrix, andx represents a sought n-dimensional nozzle control vector and the nozzlecontrol vector meets a minimization criterion of${J:=\left. {\sum\limits_{i = 1}^{i = n}\; x_{i}}\rightarrow\min \right.},$the method comprising: computer generating a defined matrixtransformation of starting constraints for a mass flow of the nozzlesand of the minimization criterion; data processing a representation of ageometric description of the matrix transformation of startingconstraints; searching, with a computer-assisted geometric searchprocedure in vector space, limiting point sets of the geometricdescription of the starting constraints; applying the matrixtransformation of minimization criterion to the points of the limitingpoint sets; and controlling the nozzles.
 2. The method according toclaim 1, wherein the matrix transformation of the starting constraintsfor the mass flow of the nozzles includes a homogenous solution of thecontrol instruction according to x_(ho)=A_(o)r, whereby A_(o):represents the n×(n−m) dimensional zero space matrix of A, and r:represents an (n−m) dimensional vector of any real numbers, the methodfurther comprising: calculating, within a scope of a use of the matrixtransformation of the minimization criterion, scalar products of avector representation of points of the limiting point set and the vector${v_{d}^{T}:=\left\lbrack {\sum\limits_{j = 1}^{n}\;{A_{0{j1}}{\sum\limits_{j = 1}^{n}\;{A_{0{j2}}\ldots{\sum\limits_{j = 1}^{n}\; A_{0{jp}}}}}}} \right\rbrack},{p:={n - m}}$and calculating an optimum-fuel solution with the aid of the vector rwhose scalar product is minimal with the vector v_(d).
 3. The methodaccording to claim 1, further comprising: converting the matrixtransformation of the starting constraints for the mass flow of thenozzles in a computer-assisted manner into allowable multi-dimensionalvalue regions; forming, to determine the limiting point sets, at leastone multi-dimensional cut set of individual allowable multi-dimensionalvalue regions; and determining the limiting point sets as those pointsets that limit the at least one cut set.
 4. The method according toclaim 3, further comprising: repeatedly projecting the allowablemulti-dimensional value regions of the dimension p on a dimension p−1,until a projection of the allowable value regions on limiting intervalsof a dimension p=1 has been achieved; and subsequently searching, with acomputer-assisted search procedure, determination of limiting point setsas a cut set of limiting intervals.
 5. A computer control method forcontrolling nozzles based upon an optimum-fuel usage for the nozzlesbased on a m-dimensional forces/torque vector, m×n-dimensional nozzlematrix, and a n-dimensional nozzle control vector that meets aminimization criterion, the method comprising: generating a definedmatrix transformation of starting constraints for a mass flow of thenozzles and of the minimization criterion; data processing arepresentation of a geometric description of the matrix transformationof starting constraints; searching, with a geometric search procedure invector space, limiting point sets of the geometric description of thestarting constraints; applying the matrix transformation of minimizationcriterion to the points of the limiting point sets; and controlling thenozzles.
 6. The method according to claim 5, wherein the controlinstruction is b=Ax, whereby b represents the desired m-dimensionalforces/torque vector, A represents the m×n-dimensional nozzle matrix,and x represents the sought n-dimensional nozzle control vector and thenozzle control vector satisfies the minimization criterion of$J:=\left. {\sum\limits_{i = 1}^{i = n}\; x_{i}}\rightarrow{\min.} \right.$7. The method according to claim 6, wherein the matrix transformation ofthe starting constraints for the mass flow of the nozzles includes ahomogenous solution of the control instruction according tox_(ho)=A_(o)r , whereby A_(o): represents the n×(n−m) dimensional zerospace matrix of A, and r: represents an (n−m) dimensional vector of realnumbers, the method further comprising: calculating, within a scope of ause of the matrix transformation of the minimization criterion, scalarproducts of a vector representation of points of the limiting point setand the vector${v_{d}^{T}:=\left\lbrack {\sum\limits_{j = 1}^{n}\;{A_{0{j1}}{\sum\limits_{j = 1}^{n}\;{A_{0{j2}}\ldots{\sum\limits_{j = 1}^{n}\; A_{0{jp}}}}}}} \right\rbrack},{p:={n - m}}$and calculating an optimum-fuel solution with the aid of the vector rwhose scalar product is minimal with the vector v_(d).
 8. The methodaccording to claim 6, further comprising: converting the matrixtransformation of the starting constraints for the mass flow of thenozzles into allowable multi-dimensional value regions; forming, todetermine the limiting point sets, at least one multi-dimensional cutset of individual allowable multi-dimensional value regions; anddetermining the limiting point sets as those point sets that limit theat least one cut set.
 9. The method according to claim 8, furthercomprising: repeatedly projecting the allowable multi-dimensional valueregions of the dimension p on a dimension p−1, until a projection of theallowable value regions on limiting intervals of a dimension p=1 hasbeen achieved; and subsequently searching a determination of limitingpoint sets as a cut set of limiting intervals.